Performing automatic recognition by a processing system of an object-of-interest within an image is important for many tasks, including such tasks as vehicle identification by license plate recognition. Accurate camera exposure control for a region occupied by the object-of-interest is crucial to achieving optimal object recognition in computer vision. Since the location of the object-of-interest is often not known a priori, existing exposure control algorithms generally use a global luminance of the image to set the exposure threshold, which often yields poor exposure result for the object-of-interest, especially when the scene in the image is not evenly illuminated. In some existing systems, more weight or bias is put towards a designated area of the image, such as the center of the image, where the object-of-interest may have a higher likelihood to be located. This strategy will only work when the object-of-interest is limited to a particular location by some means. In the more general case where the object-of-interest can be anywhere in the image frame, using an average of a designated area, such as the center of the image, may do more harm than good.
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